System and method for parallel flushing with bucketized data

ABSTRACT

A method, computer program product, and computer system for organizing a plurality of log records into a plurality of buckets, wherein each bucket is associated with a range of a plurality of ranges within a backing store. A bucket of the plurality of buckets from which a portion of the log records of the plurality of log records are to be flushed may be selected. The portion of the log records may be organized into parallel flush jobs. The portion of the log records may be flushed to the backing store in parallel.

BACKGROUND

In a flat chronological log, there may be a large number of records thatneed to be flushed out the backing store. The backing store may havelimitations on the amount of work that can be submitted in a job. Thebacking store may also have limitations on the overlapping of LogicalBlock Address (LBA) ranges the jobs submitted. The logging service on aprimary node may be responsible for driving the flush. Flushes may beinitiated when the low watermark has been reached, or if an extent flushoperation for copy, delete, or flush is requested from a client.Generally, this is a single thread where only one flush occurs at apoint in time. Because log space may typically only be freed when pagesare overwritten or flushed, the storage system performance may rely onthe rate at which these log pages are flushed. If flushing cannot keepup with the rate of incoming writes, user I/O may suffer in performance.

BRIEF SUMMARY OF DISCLOSURE

In one example implementation, a method, performed by one or morecomputing devices, may include but is not limited to organizing aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store. A bucket of the plurality of buckets from which a portionof the log records of the plurality of log records are to be flushed maybe selected. The portion of the log records may be organized intoparallel flush jobs. The portion of the log records may be flushed tothe backing store in parallel.

One or more of the following example features may be included. Theportion of the log records may include destination logical block address(LBA) of the backing store associated with the range, and wherein theportion of the log records may include log sequence numbers (LSNs).Selecting the bucket of the plurality of buckets from which the portionof the log records of the plurality of log records are to be flushed mayinclude selecting the bucket with a lowest LSN from a tree. Flushing theportion of the log records to the backing store in parallel may includeinvalidating the portion of the log records in memory. Flushing theportion of the log records to the backing store in parallel may furtherinclude removing the portion of the log records from the bucket.Flushing the portion of the log records to the backing store in parallelmay further include removing the portion of the log records from thetree. The tree may be rebalanced.

In another example implementation, a computing system may include one ormore processors and one or more memories configured to performoperations that may include but are not limited to organizing aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store. A bucket of the plurality of buckets from which a portionof the log records of the plurality of log records are to be flushed maybe selected. The portion of the log records may be organized intoparallel flush jobs. The portion of the log records may be flushed tothe backing store in parallel.

One or more of the following example features may be included. Theportion of the log records may include destination logical block address(LBA) of the backing store associated with the range, and wherein theportion of the log records may include log sequence numbers (LSNs).Selecting the bucket of the plurality of buckets from which the portionof the log records of the plurality of log records are to be flushed mayinclude selecting the bucket with a lowest LSN from a tree. Flushing theportion of the log records to the backing store in parallel may includeinvalidating the portion of the log records in memory. Flushing theportion of the log records to the backing store in parallel may furtherinclude removing the portion of the log records from the bucket.Flushing the portion of the log records to the backing store in parallelmay further include removing the portion of the log records from thetree. The tree may be rebalanced.

In another example implementation, a computer program product may resideon a computer readable storage medium having a plurality of instructionsstored thereon which, when executed across one or more processors, maycause at least a portion of the one or more processors to performoperations that may include but are not limited to organizing aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store. A bucket of the plurality of buckets from which a portionof the log records of the plurality of log records are to be flushed maybe selected. The portion of the log records may be organized intoparallel flush jobs. The portion of the log records may be flushed tothe backing store in parallel.

One or more of the following example features may be included. Theportion of the log records may include destination logical block address(LBA) of the backing store associated with the range, and wherein theportion of the log records may include log sequence numbers (LSNs).Selecting the bucket of the plurality of buckets from which the portionof the log records of the plurality of log records are to be flushed mayinclude selecting the bucket with a lowest LSN from a tree. Flushing theportion of the log records to the backing store in parallel may includeinvalidating the portion of the log records in memory. Flushing theportion of the log records to the backing store in parallel may furtherinclude removing the portion of the log records from the bucket.Flushing the portion of the log records to the backing store in parallelmay further include removing the portion of the log records from thetree. The tree may be rebalanced.

The details of one or more example implementations are set forth in theaccompanying drawings and the description below. Other possible examplefeatures and/or possible example advantages will become apparent fromthe description, the drawings, and the claims. Some implementations maynot have those possible example features and/or possible exampleadvantages, and such possible example features and/or possible exampleadvantages may not necessarily be required of some implementations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example diagrammatic view of a flush process coupled to anexample distributed computing network according to one or more exampleimplementations of the disclosure;

FIG. 2 is an example diagrammatic view of a storage system of FIG. 1according to one or more example implementations of the disclosure;

FIG. 3 is an example diagrammatic view of a storage target of FIG. 1according to one or more example implementations of the disclosure;

FIG. 4 is an example flowchart of a flush process according to one ormore example implementations of the disclosure;

FIG. 5 is an example diagrammatic view of a bucket data structureaccording to one or more example implementations of the disclosure;

FIG. 6 is an example diagrammatic view of a data structure used for achain/list of records according to one or more example implementationsof the disclosure;

FIG. 7 is an example diagrammatic view of a chain of records accordingto one or more example implementations of the disclosure;

FIG. 8 is an example diagrammatic view of a general flow for classifyinglog records and adding buckets into an LSNTree according to one or moreexample implementations of the disclosure;

FIG. 9 is an example diagrammatic view of a bucket organizationaccording to one or more example implementations of the disclosure;

FIG. 10 is an example diagrammatic view of a layout showing how bucketsmay be organized by lowest LSN with a tree according to one or moreexample implementations of the disclosure; and

FIG. 11 is an example diagrammatic view of a general flow for flow 1100associated with a flush process that describes the sequence of eventsthat may occur on a flush request according to one or more exampleimplementations of the disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION System Overview:

In some implementations, the present disclosure may be embodied as amethod, system, or computer program product. Accordingly, in someimplementations, the present disclosure may take the form of an entirelyhardware implementation, an entirely software implementation (includingfirmware, resident software, micro-code, etc.) or an implementationcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore, insome implementations, the present disclosure may take the form of acomputer program product on a computer-usable storage medium havingcomputer-usable program code embodied in the medium.

In some implementations, any suitable computer usable or computerreadable medium (or media) may be utilized. The computer readable mediummay be a computer readable signal medium or a computer readable storagemedium. The computer-usable, or computer-readable, storage medium(including a storage device associated with a computing device or clientelectronic device) may be, for example, but is not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, device, or any suitable combination ofthe foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a digital versatile disk (DVD), a static randomaccess memory (SRAM), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, a media such as those supportingthe internet or an intranet, or a magnetic storage device. Note that thecomputer-usable or computer-readable medium could even be a suitablemedium upon which the program is stored, scanned, compiled, interpreted,or otherwise processed in a suitable manner, if necessary, and thenstored in a computer memory. In the context of the present disclosure, acomputer-usable or computer-readable, storage medium may be any tangiblemedium that can contain or store a program for use by or in connectionwith the instruction execution system, apparatus, or device.

In some implementations, a computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. In someimplementations, such a propagated signal may take any of a variety offorms, including, but not limited to, electro-magnetic, optical, or anysuitable combination thereof. In some implementations, the computerreadable program code may be transmitted using any appropriate medium,including but not limited to the internet, wireline, optical fibercable, RF, etc. In some implementations, a computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

In some implementations, computer program code for carrying outoperations of the present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java®, Smalltalk, C++ or the like.Java® and all Java-based trademarks and logos are trademarks orregistered trademarks of Oracle and/or its affiliates. However, thecomputer program code for carrying out operations of the presentdisclosure may also be written in conventional procedural programminglanguages, such as the “C” programming language, PASCAL, or similarprogramming languages, as well as in scripting languages such asJavascript, PERL, or Python. The program code may execute entirely onthe user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough a local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theinternet using an Internet Service Provider). In some implementations,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGAs) or other hardwareaccelerators, micro-controller units (MCUs), or programmable logicarrays (PLAs) may execute the computer readable programinstructions/code by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

In some implementations, the flowchart and block diagrams in the figuresillustrate the architecture, functionality, and operation of possibleimplementations of apparatus (systems), methods and computer programproducts according to various implementations of the present disclosure.Each block in the flowchart and/or block diagrams, and combinations ofblocks in the flowchart and/or block diagrams, may represent a module,segment, or portion of code, which comprises one or more executablecomputer program instructions for implementing the specified logicalfunction(s)/act(s). These computer program instructions may be providedto a processor of a general purpose computer, special purpose computer,or other programmable data processing apparatus to produce a machine,such that the computer program instructions, which may execute via theprocessor of the computer or other programmable data processingapparatus, create the ability to implement one or more of thefunctions/acts specified in the flowchart and/or block diagram block orblocks or combinations thereof. It should be noted that, in someimplementations, the functions noted in the block(s) may occur out ofthe order noted in the figures (or combined or omitted). For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

In some implementations, these computer program instructions may also bestored in a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks or combinations thereof.

In some implementations, the computer program instructions may also beloaded onto a computer or other programmable data processing apparatusto cause a series of operational steps to be performed (not necessarilyin a particular order) on the computer or other programmable apparatusto produce a computer implemented process such that the instructionswhich execute on the computer or other programmable apparatus providesteps for implementing the functions/acts (not necessarily in aparticular order) specified in the flowchart and/or block diagram blockor blocks or combinations thereof.

Referring now to the example implementation of FIG. 1, there is shownflush process 10 that may reside on and may be executed by a computer(e.g., computer 12), which may be connected to a network (e.g., network14) (e.g., the internet or a local area network). Examples of computer12 (and/or one or more of the client electronic devices noted below) mayinclude, but are not limited to, a storage system (e.g., a NetworkAttached Storage (NAS) system, a Storage Area Network (SAN)), a personalcomputer(s), a laptop computer(s), mobile computing device(s), a servercomputer, a series of server computers, a mainframe computer(s), or acomputing cloud(s). As is known in the art, a SAN may include one ormore of the client electronic devices, including a RAID device and a NASsystem. In some implementations, each of the aforementioned may begenerally described as a computing device. In certain implementations, acomputing device may be a physical or virtual device. In manyimplementations, a computing device may be any device capable ofperforming operations, such as a dedicated processor, a portion of aprocessor, a virtual processor, a portion of a virtual processor,portion of a virtual device, or a virtual device. In someimplementations, a processor may be a physical processor or a virtualprocessor. In some implementations, a virtual processor may correspondto one or more parts of one or more physical processors. In someimplementations, the instructions/logic may be distributed and executedacross one or more processors, virtual or physical, to execute theinstructions/logic. Computer 12 may execute an operating system, forexample, but not limited to, Microsoft® Windows®; Mac® OS X®; Red Hat®Linux®, Windows® Mobile, Chrome OS, Blackberry OS, Fire OS, or a customoperating system. (Microsoft and Windows are registered trademarks ofMicrosoft Corporation in the United States, other countries or both; Macand OS X are registered trademarks of Apple Inc. in the United States,other countries or both; Red Hat is a registered trademark of Red HatCorporation in the United States, other countries or both; and Linux isa registered trademark of Linus Torvalds in the United States, othercountries or both).

In some implementations, as will be discussed below in greater detail, aflush process, such as flush process 10 of FIG. 1, may organize aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store. A bucket of the plurality of buckets from which a portionof the log records of the plurality of log records are to be flushed maybe selected. The portion of the log records may be organized intoparallel flush jobs. The portion of the log records may be flushed tothe backing store in parallel.

In some implementations, the instruction sets and subroutines of flushprocess 10, which may be stored on storage device, such as storagedevice 16, coupled to computer 12, may be executed by one or moreprocessors and one or more memory architectures included within computer12. In some implementations, storage device 16 may include but is notlimited to: a hard disk drive; all forms of flash memory storagedevices; a tape drive; an optical drive; a RAID array (or other array);a random access memory (RAM); a read-only memory (ROM); or combinationthereof. In some implementations, storage device 16 may be organized asan extent, an extent pool, a RAID extent (e.g., an example 4D+1P R5,where the RAID extent may include, e.g., five storage device extentsthat may be allocated from, e.g., five different storage devices), amapped RAID (e.g., a collection of RAID extents), or combinationthereof.

In some implementations, network 14 may be connected to one or moresecondary networks (e.g., network 18), examples of which may include butare not limited to: a local area network; a wide area network or othertelecommunications network facility; or an intranet, for example. Thephrase “telecommunications network facility,” as used herein, may referto a facility configured to transmit, and/or receive transmissionsto/from one or more mobile client electronic devices (e.g., cellphones,etc.) as well as many others.

In some implementations, computer 12 may include a data store, such as adatabase (e.g., relational database, object-oriented database,triplestore database, etc.) and may be located within any suitablememory location, such as storage device 16 coupled to computer 12. Insome implementations, data, metadata, information, etc. describedthroughout the present disclosure may be stored in the data store. Insome implementations, computer 12 may utilize any known databasemanagement system such as, but not limited to, DB2, in order to providemulti-user access to one or more databases, such as the above notedrelational database. In some implementations, the data store may also bea custom database, such as, for example, a flat file database or an XMLdatabase. In some implementations, any other form(s) of a data storagestructure and/or organization may also be used. In some implementations,flush process 10 may be a component of the data store, a standaloneapplication that interfaces with the above noted data store and/or anapplet/application that is accessed via client applications 22, 24, 26,28. In some implementations, the above noted data store may be, in wholeor in part, distributed in a cloud computing topology. In this way,computer 12 and storage device 16 may refer to multiple devices, whichmay also be distributed throughout the network.

In some implementations, computer 12 may execute a storage managementapplication (e.g., storage management application 21), examples of whichmay include, but are not limited to, e.g., a storage system application,a cloud computing application, a data synchronization application, adata migration application, a garbage collection application, or otherapplication that allows for the implementation and/or management of datain a clustered (or non-clustered) environment (or the like). In someimplementations, flush process 10 and/or storage management application21 may be accessed via one or more of client applications 22, 24, 26,28. In some implementations, flush process 10 may be a standaloneapplication, or may be an applet/application/script/extension that mayinteract with and/or be executed within storage management application21, a component of storage management application 21, and/or one or moreof client applications 22, 24, 26, 28. In some implementations, storagemanagement application 21 may be a standalone application, or may be anapplet/application/script/extension that may interact with and/or beexecuted within flush process 10, a component of flush process 10,and/or one or more of client applications 22, 24, 26, 28. In someimplementations, one or more of client applications 22, 24, 26, 28 maybe a standalone application, or may be anapplet/application/script/extension that may interact with and/or beexecuted within and/or be a component of flush process 10 and/or storagemanagement application 21. Examples of client applications 22, 24, 26,28 may include, but are not limited to, e.g., a storage systemapplication, a cloud computing application, a data synchronizationapplication, a data migration application, a garbage collectionapplication, or other application that allows for the implementationand/or management of data in a clustered (or non-clustered) environment(or the like), a standard and/or mobile web browser, an emailapplication (e.g., an email client application), a textual and/or agraphical user interface, a customized web browser, a plugin, anApplication Programming Interface (API), or a custom application. Theinstruction sets and subroutines of client applications 22, 24, 26, 28,which may be stored on storage devices 30, 32, 34, 36, coupled to clientelectronic devices 38, 40, 42, 44, may be executed by one or moreprocessors and one or more memory architectures incorporated into clientelectronic devices 38, 40, 42, 44.

In some implementations, one or more of storage devices 30, 32, 34, 36,may include but are not limited to: hard disk drives; flash drives, tapedrives; optical drives; RAID arrays; random access memories (RAM); andread-only memories (ROM). Examples of client electronic devices 38, 40,42, 44 (and/or computer 12) may include, but are not limited to, apersonal computer (e.g., client electronic device 38), a laptop computer(e.g., client electronic device 40), a smart/data-enabled, cellularphone (e.g., client electronic device 42), a notebook computer (e.g.,client electronic device 44), a tablet, a server, a television, a smarttelevision, a smart speaker, an Internet of Things (IoT) device, a media(e.g., video, photo, etc.) capturing device, and a dedicated networkdevice. Client electronic devices 38, 40, 42, 44 may each execute anoperating system, examples of which may include but are not limited to,Android™, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, Windows® Mobile,Chrome OS, Blackberry OS, Fire OS, or a custom operating system.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality offlush process 10 (and vice versa). Accordingly, in some implementations,flush process 10 may be a purely server-side application, a purelyclient-side application, or a hybrid server-side/client-side applicationthat is cooperatively executed by one or more of client applications 22,24, 26, 28 and/or flush process 10.

In some implementations, one or more of client applications 22, 24, 26,28 may be configured to effectuate some or all of the functionality ofstorage management application 21 (and vice versa). Accordingly, in someimplementations, storage management application 21 may be a purelyserver-side application, a purely client-side application, or a hybridserver-side/client-side application that is cooperatively executed byone or more of client applications 22, 24, 26, 28 and/or storagemanagement application 21. As one or more of client applications 22, 24,26, 28, flush process 10, and storage management application 21, takensingly or in any combination, may effectuate some or all of the samefunctionality, any description of effectuating such functionality viaone or more of client applications 22, 24, 26, 28, flush process 10,storage management application 21, or combination thereof, and anydescribed interaction(s) between one or more of client applications 22,24, 26, 28, flush process 10, storage management application 21, orcombination thereof to effectuate such functionality, should be taken asan example only and not to limit the scope of the disclosure.

In some implementations, one or more of users 46, 48, 50, 52 may accesscomputer 12 and flush process 10 (e.g., using one or more of clientelectronic devices 38, 40, 42, 44) directly through network 14 orthrough secondary network 18. Further, computer 12 may be connected tonetwork 14 through secondary network 18, as illustrated with phantomlink line 54. Flush process 10 may include one or more user interfaces,such as browsers and textual or graphical user interfaces, through whichusers 46, 48, 50, 52 may access flush process 10.

In some implementations, the various client electronic devices may bedirectly or indirectly coupled to network 14 (or network 18). Forexample, client electronic device 38 is shown directly coupled tonetwork 14 via a hardwired network connection. Further, clientelectronic device 44 is shown directly coupled to network 18 via ahardwired network connection. Client electronic device 40 is shownwirelessly coupled to network 14 via wireless communication channel 56established between client electronic device 40 and wireless accesspoint (i.e., WAP) 58, which is shown directly coupled to network 14. WAP58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,802.11ac, Wi-Fi®, RFID, and/or Bluetooth™ (including Bluetooth™ LowEnergy) device that is capable of establishing wireless communicationchannel 56 between client electronic device 40 and WAP 58. Clientelectronic device 42 is shown wirelessly coupled to network 14 viawireless communication channel 60 established between client electronicdevice 42 and cellular network/bridge 62, which is shown by exampledirectly coupled to network 14.

In some implementations, some or all of the IEEE 802.11x specificationsmay use Ethernet protocol and carrier sense multiple access withcollision avoidance (i.e., CSMA/CA) for path sharing. The various802.11x specifications may use phase-shift keying (i.e., PSK) modulationor complementary code keying (i.e., CCK) modulation, for example.Bluetooth™ (including Bluetooth™ Low Energy) is a telecommunicationsindustry specification that allows, e.g., mobile phones, computers,smart phones, and other electronic devices to be interconnected using ashort-range wireless connection. Other forms of interconnection (e.g.,Near Field Communication (NFC)) may also be used.

In some implementations, various I/O requests (e.g., I/O request 15) maybe sent from, e.g., client applications 22, 24, 26, 28 to, e.g.,computer 12. Examples of I/O request 15 may include but are not limitedto, data write requests (e.g., a request that content be written tocomputer 12) and data read requests (e.g., a request that content beread from computer 12).

Data Storage System:

Referring also to the example implementation of FIGS. 2-3 (e.g., wherecomputer 12 may be configured as a data storage system), computer 12 mayinclude storage processor 100 and a plurality of storage targets (e.g.,storage targets 102, 104, 106, 108, 110). In some implementations,storage targets 102, 104, 106, 108, 110 may include any of theabove-noted storage devices. In some implementations, storage targets102, 104, 106, 108, 110 may be configured to provide various levels ofperformance and/or high availability. For example, storage targets 102,104, 106, 108, 110 may be configured to form a non-fully-duplicativefault-tolerant data storage system (such as a non-fully-duplicative RAIDdata storage system), examples of which may include but are not limitedto: RAID 3 arrays, RAID 4 arrays, RAID 5 arrays, and/or RAID 6 arrays.It will be appreciated that various other types of RAID arrays may beused without departing from the scope of the present disclosure.

While in this particular example, computer 12 is shown to include fivestorage targets (e.g., storage targets 102, 104, 106, 108, 110), this isfor example purposes only and is not intended limit the presentdisclosure. For instance, the actual number of storage targets may beincreased or decreased depending upon, e.g., the level ofredundancy/performance/capacity required.

Further, the storage targets (e.g., storage targets 102, 104, 106, 108,110) included with computer 12 may be configured to form a plurality ofdiscrete storage arrays. For instance, and assuming for example purposesonly that computer 12 includes, e.g., ten discrete storage targets, afirst five targets (of the ten storage targets) may be configured toform a first RAID array and a second five targets (of the ten storagetargets) may be configured to form a second RAID array.

In some implementations, one or more of storage targets 102, 104, 106,108, 110 may be configured to store coded data (e.g., via storagemanagement process 21), wherein such coded data may allow for theregeneration of data lost/corrupted on one or more of storage targets102, 104, 106, 108, 110. Examples of such coded data may include but isnot limited to parity data and Reed-Solomon data. Such coded data may bedistributed across all of storage targets 102, 104, 106, 108, 110 or maybe stored within a specific storage target.

Examples of storage targets 102, 104, 106, 108, 110 may include one ormore data arrays, wherein a combination of storage targets 102, 104,106, 108, 110 (and any processing/control systems associated withstorage management application 21) may form data array 112.

The manner in which computer 12 is implemented may vary depending upone.g., the level of redundancy/performance/capacity required. Forexample, computer 12 may be configured as a SAN (i.e., a Storage AreaNetwork), in which storage processor 100 may be, e.g., a dedicatedcomputing system and each of storage targets 102, 104, 106, 108, 110 maybe a RAID device. An example of storage processor 100 may include but isnot limited to a VPLEX™ system offered by Dell EMC™ of Hopkinton, Mass.

In the example where computer 12 is configured as a SAN, the variouscomponents of computer 12 (e.g., storage processor 100, and storagetargets 102, 104, 106, 108, 110) may be coupled using networkinfrastructure 114, examples of which may include but are not limited toan Ethernet (e.g., Layer 2 or Layer 3) network, a fiber channel network,an InfiniBand network, or any other circuit switched/packet switchednetwork.

As discussed above, various I/O requests (e.g., I/O request 15) may begenerated. For example, these I/O requests may be sent from, e.g.,client applications 22, 24, 26, 28 to, e.g., computer 12.Additionally/alternatively (e.g., when storage processor 100 isconfigured as an application server or otherwise), these I/O requestsmay be internally generated within storage processor 100 (e.g., viastorage management process 21). Examples of I/O request 15 may includebut are not limited to data write request 116 (e.g., a request thatcontent 118 be written to computer 12) and data read request 120 (e.g.,a request that content 118 be read from computer 12).

In some implementations, during operation of storage processor 100,content 118 to be written to computer 12 may be received and/orprocessed by storage processor 100 (e.g., via storage management process21). Additionally/alternatively (e.g., when storage processor 100 isconfigured as an application server or otherwise), content 118 to bewritten to computer 12 may be internally generated by storage processor100 (e.g., via storage management process 21).

As discussed above, the instruction sets and subroutines of storagemanagement application 21, which may be stored on storage device 16included within computer 12, may be executed by one or more processorsand one or more memory architectures included with computer 12.Accordingly, in addition to being executed on storage processor 100,some or all of the instruction sets and subroutines of storagemanagement application 21 (and/or flush process 10) may be executed byone or more processors and one or more memory architectures includedwith data array 112.

In some implementations, storage processor 100 may include front endcache memory system 122. Examples of front end cache memory system 122may include but are not limited to a volatile, solid-state, cache memorysystem (e.g., a dynamic RAM cache memory system), a non-volatile,solid-state, cache memory system (e.g., a flash-based, cache memorysystem), and/or any of the above-noted storage devices.

In some implementations, storage processor 100 may initially storecontent 118 within front end cache memory system 122. Depending upon themanner in which front end cache memory system 122 is configured, storageprocessor 100 (e.g., via storage management process 21) may immediatelywrite content 118 to data array 112 (e.g., if front end cache memorysystem 122 is configured as a write-through cache) or may subsequentlywrite content 118 to data array 112 (e.g., if front end cache memorysystem 122 is configured as a write-back cache).

In some implementations, one or more of storage targets 102, 104, 106,108, 110 may include a backend cache memory system. Examples of thebackend cache memory system may include but are not limited to avolatile, solid-state, cache memory system (e.g., a dynamic RAM cachememory system), a non-volatile, solid-state, cache memory system (e.g.,a flash-based, cache memory system), and/or any of the above-notedstorage devices.

Storage Targets:

As discussed above, one or more of storage targets 102, 104, 106, 108,110 may be a RAID device. For instance, and referring also to FIG. 3,there is shown example target 150, wherein target 150 may be one exampleimplementation of a RAID implementation of, e.g., storage target 102,storage target 104, storage target 106, storage target 108, and/orstorage target 110. An example of target 150 may include but is notlimited to a VNX™ system offered by Dell EMC™ of Hopkinton, Mass.Examples of storage devices 154, 156, 158, 160, 162 may include one ormore electro-mechanical hard disk drives, one or more solid-state/flashdevices, and/or any of the above-noted storage devices. It will beappreciated that while the term “disk” or “drive” may be usedthroughout, these may refer to and be used interchangeably with anytypes of appropriate storage devices as the context and functionality ofthe storage device permits.

In some implementations, target 150 may include storage processor 152and a plurality of storage devices (e.g., storage devices 154, 156, 158,160, 162). Storage devices 154, 156, 158, 160, 162 may be configured toprovide various levels of performance and/or high availability (e.g.,via storage management process 21). For example, one or more of storagedevices 154, 156, 158, 160, 162 (or any of the above-noted storagedevices) may be configured as a RAID 0 array, in which data is stripedacross storage devices. By striping data across a plurality of storagedevices, improved performance may be realized. However, RAID 0 arraysmay not provide a level of high availability. Accordingly, one or moreof storage devices 154, 156, 158, 160, 162 (or any of the above-notedstorage devices) may be configured as a RAID 1 array, in which data ismirrored between storage devices. By mirroring data between storagedevices, a level of high availability may be achieved as multiple copiesof the data may be stored within storage devices 154, 156, 158, 160,162.

While storage devices 154, 156, 158, 160, 162 are discussed above asbeing configured in a RAID 0 or RAID 1 array, this is for examplepurposes only and not intended to limit the present disclosure, as otherconfigurations are possible. For example, storage devices 154, 156, 158,160, 162 may be configured as a RAID 3, RAID 4, RAID 5 or RAID 6 array.

While in this particular example, target 150 is shown to include fivestorage devices (e.g., storage devices 154, 156, 158, 160, 162), this isfor example purposes only and not intended to limit the presentdisclosure. For instance, the actual number of storage devices may beincreased or decreased depending upon, e.g., the level ofredundancy/performance/capacity required.

In some implementations, one or more of storage devices 154, 156, 158,160, 162 may be configured to store (e.g., via storage managementprocess 21) coded data, wherein such coded data may allow for theregeneration of data lost/corrupted on one or more of storage devices154, 156, 158, 160, 162. Examples of such coded data may include but arenot limited to parity data and Reed-Solomon data. Such coded data may bedistributed across all of storage devices 154, 156, 158, 160, 162 or maybe stored within a specific storage device.

The manner in which target 150 is implemented may vary depending upone.g., the level of redundancy/performance/capacity required. Forexample, target 150 may be a RAID device in which storage processor 152is a RAID controller card and storage devices 154, 156, 158, 160, 162are individual “hot-swappable” hard disk drives. Another example oftarget 150 may be a RAID system, examples of which may include but arenot limited to an NAS (i.e., Network Attached Storage) device or a SAN(i.e., Storage Area Network).

In some implementations, storage target 150 may execute all or a portionof storage management application 21. The instruction sets andsubroutines of storage management application 21, which may be stored ona storage device (e.g., storage device 164) coupled to storage processor152, may be executed by one or more processors and one or more memoryarchitectures included with storage processor 152. Storage device 164may include but is not limited to any of the above-noted storagedevices.

As discussed above, computer 12 may be configured as a SAN, whereinstorage processor 100 may be a dedicated computing system and each ofstorage targets 102, 104, 106, 108, 110 may be a RAID device.Accordingly, when storage processor 100 processes data requests 116,120, storage processor 100 (e.g., via storage management process 21) mayprovide the appropriate requests/content (e.g., write request 166,content 168 and read request 170) to, e.g., storage target 150 (which isrepresentative of storage targets 102, 104, 106, 108 and/or 110).

In some implementations, during operation of storage processor 152,content 168 to be written to target 150 may be processed by storageprocessor 152 (e.g., via storage management process 21). Storageprocessor 152 may include cache memory system 172. Examples of cachememory system 172 may include but are not limited to a volatile,solid-state, cache memory system (e.g., a dynamic RAM cache memorysystem) and/or a non-volatile, solid-state, cache memory system (e.g., aflash-based, cache memory system). During operation of storage processor152, content 168 to be written to target 150 may be received by storageprocessor 152 (e.g., via storage management process 21) and initiallystored (e.g., via storage management process 21) within front end cachememory system 172.

As noted above, in a flat chronological log, there may be a large numberof records that need to be flushed out the backing store. The backingstore may have limitations on the amount of work that can be submittedin a job. The backing store may also have limitations on the overlappingof Logical Block Address (LBA) ranges the jobs submitted. The loggingservice on a primary node may be responsible for driving the flush.Flushes may be initiated when the low watermark has been reached, or ifan extent flush operation for copy, delete, or flush is requested from aclient. Generally, this is a single thread where only one flush occursat a point in time. Because log space may typically only be freed whenpages are overwritten or flushed, the storage system performance mayrely on the rate at which these log pages are flushed. If flushingcannot keep up with the rate of incoming writes, user I/O may suffer inperformance. As such, as will be discussed below, unlike known singlethreaded approaches, the flush rate may be improved by issuing multipleflushes in parallel to perform the work. The present disclosure mayprovide a solution to divide the flush work among multiple parallel jobsto improve performance, while also abiding by certain backing storelimitations such as overlapping of LBA ranges within parallel jobs.

The Flush Process:

As discussed above and referring also at least to the exampleimplementations of FIGS. 4-11, flush process 10 may organize 400 aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store. Flush process 10 may select 402 a bucket of the pluralityof buckets from which a portion of the log records of the plurality oflog records are to be flushed. Flush process 10 may organize 404 theportion of the log records into parallel flush jobs. Flush process 10may flush 406 the portion of the log records to the backing store inparallel.

In some implementations, flush process 10 may stage writes into a log inchronological order, wherein each write may have a log record of aplurality of log records describing data of the write. For example,incoming writes may be staged into a log in sequential order. In someimplementations, the log record may include a destination logical blockaddress (LBA) of the backing store associated with the range within thebacking store, as well as a log sequence number (LSN). For example, eachwrite may have a record describing the data. Such information mayinclude, e.g., the destination LBA of the backing store when the writeis de-staged from the log, as well as a monotonically increasing LSNthat may define the record's location in the log (e.g., circular logring) as well as its chronological order within the ring.

In some implementations, flush process 10 may organize 400 a pluralityof log records into a plurality of buckets, wherein each bucket may beassociated with a range of a plurality of ranges within a backing store.For example, the portion of the log records may include destinationlogical block address (LBA) of the backing store associated with therange, and wherein the portion of the log records may include logsequence numbers (LSNs). For instance, in some implementations, eachbucket of the plurality of buckets may include two keys respectively.For example, the log records may be organized 402 into buckets thatpertain to a particular logical range within the backing store. Thelogical range that each bucket covers may be of the same size. In someimplementations, a first key of the two keys may include a starting LBAof the range within the backing store, and the first key of the two keysmay be used to one of reference the bucket and create a new bucket ifthe bucket does not exist. For example, the buckets may contain twokeys, one of which may be the starting LBA of the LBA range the bucketwill cover. An example bucket store keyed/hashed by the LBA may be usedto reference a bucket when adding records into an existing bucket, orcreating a new bucket when a corresponding bucket does not exist. Thesebuckets may localize pages into LBA ranges that may be optimized for thebacking store.

In some implementations, flush process 10 may flush the log record ofthe plurality of log records from the bucket of the plurality of bucketsto the backing store at a location and in an order determined basedupon, at least in part, the two keys included with the bucket, where asecond key of the two keys may include a lowest log sequence number ofany records within the bucket, which may be used to create a tree forflush ordering. For instance, selecting the bucket of the plurality ofbuckets from which the portion of the log records of the plurality oflog records are to be flushed may include selecting 408 the bucket witha lowest LSN from a tree. For example, another key may be the lowest LSNof the records within the bucket. This key may be used to create theLSNTree for flush ordering. During the flush, the records from the pagesmay be taken from the buckets with the lowest LSN from the LSNTree, thusmaintaining priority on tail movement while also localizing pages. Therecords belonging to a bucket may be chained through the LSNKey, whichmay be the lowest LSN and chronologically first record in the bucket.Referring at least to the example implementation of FIG. 5, a high leveloverview of the bucket data structures 500 is shown that may be used forthe lookup of the classification of the records into buckets and theorganization of the buckets for flush 404 ordering.

Referring also to the example implementation of FIG. 6, an example datastructure 600 used for a chain/list of records in buckets is shown. Inthe example, the LSNKey may be the lowest LSN in the bucket. TheLogRecordLinkArray may be an array containing link data for each logrecord. Similarly to how the LSN self describes the element's locationwithin the ring, the index of each element within the LogRecordLinkArraymay mathematically corresponds to a particular LSN. LogRecordLink maycontain a previous LSN or may be invalid if none exists and a next LSNor may be invalid if none exists. Starting with the LSNKey of a bucket,the corresponding LogRecordLinks may thus form a linked list describinga chain of log records belonging to a particular bucket in LSN order.

Referring at least to the example implementation of FIG. 7, an exampleof a chain of records 700 in a bucket is shown. The chain may beessentially a double linked list within an array in which the indicescorrespond to a particular LSN. The bucket's LSNKey may be the firstrecord with the chain.

In some implementations, records may be classified from the log ringinto buckets during background processing in order to save on memory,throttle how much preference is given to records that occur later intime, and ease of implementation for maintaining proper ordering on LSNin the LogRecordLink chain. It may be possible to classify records oningest. When the log record is classified, they may be processed inchronological LSN order. A lookup may be done into the bucket store,which may be a hash table or a tree for example. The lookup may involvecalculating the target bucket LBAkey from the record's LBA with theequation such as, e.g., int(recordLBA/BucketSize)*BucketSize. Forexample, if a bucket were of size 10 and the LBA of a record was 22, theLBAKey of the target bucket may be 20. If the LBA of a record was 2, theLBAKey of the target bucket may be 0. The lookup into the bucket storemay yield a valid pointer to the target bucket or NULL if the targetbucket is not found. If a bucket is found in the bucket store, therecord may be added to the existing bucket by adding a LogRecordLink tothe highest LSN within the bucket.

For example, and referring to the example implementation of FIG. 8,which builds upon FIG. 7, it is demonstrated the changes required if anew record at LSN 7 was to be added to the chain. In the example, theNextLSN of LogRecordLink at LSN/index 6 may be set to 7, the PrevLSN ofLogRecordLink at LSN/index 7 may be set to 6, and the HighestLSN ofbucket X may be set to 7. The chain of records in bucket X may thusbecome 1, 2, 4, 6, 7. The tree does not need to be rebalanced since theLSNKey does not change and no new nodes are added. However, if no bucketis found in the bucket store, a new bucket may be created, the LSNKeyand HighestLSN may be set to the LSN of the log record, thecorresponding LogRecordLink may get initialized where PrevLSN andNextLSN are set to invalid, the bucket pointer may be set within theLogRecordLink, the bucket may be added to the bucket store, and thebucket may be added into the LSN Tree. The tree may need to berebalanced periodically either on insertion or deletion, since addingand deleting nodes may create an imbalanced tree. The general flow 800for classifying log records and adding buckets into the LSNTree is shownin FIG. 8.

In some implementations, the records within a bucket cannot span acrosstwo parallel flush requests and the log should prioritize flushing olderelements in order to reclaim log space. For each of the partitionedareas, a flush work request may be spawned off and all of the flush workrequests may be executed in parallel. The partitioning of this workshould consider efficiency and performance in the Mapper as well as inthe log. Parameters such as, e.g., maximum number of pages to processwithin the log, maximum number of pages per parallel flush, consumptionof log space and maximum number of parallel flushes may be used todetermine the behavior of the parallel flush. These parameters may beconfigurable and in some cases dynamic also. Some known log scanimplementations had some drawbacks. For instance, it may be possiblethat pages Mapper would like flushed together may not necessarily beflushed together if the log offsets are too far apart. Also, since notall pages are necessarily flushed, this work may need to be performedagain on each flush request. Organizing the records into buckets mayprovide the best effort on the localization on a larger range of recordsthat may be optimal for the backing store to process. Because thebuckets may be processed in LSN order, this may also guarantee tailmovement.

In some implementations, the fully committed records may be classifiedinto buckets and organized in an LSNTree. For example, and referring atleast to the example implementation of FIG. 9, an example bucketorganization 900 is shown. In the example, FIG. 9 shows how the recordsin LSN order are organized into buckets based on LBA ranges. The list ofRecordLinks within each bucket maintain the LSN order and may be placedinto the LSNTree based on the lowest LSN, which may be the oldest recordpertaining to that range. The classification may be periodically done asa background operation prior to selection of pages for flushing, so thatthe tree is ready to be processed. The classification of records may belimited to a certain size to ensure that preference of tail movement ismaintained over localization of pages.

In some implementations, flush process 10 may select 402 a bucket of theplurality of buckets from which a portion of the log records of theplurality of log records are to be flushed. In some implementations,flush process 10 may organize 404 the portion of the log records intoparallel flush jobs. For example, and referring at least to the exampleimplementation of FIG. 10, an example layout 1000 shows how buckets maybe organized by lowest LSN with a tree; that is, how these records areselected 402 from the buckets in the LSNTree for flushing. For instance,the bucket with the lowest LSN may be selected from the LSNTree.Although the chain of records in the buckets may be in LSN order, therecords of a bucket may be reorganized 404 in the parallel flush jobs(e.g., parallel flush 1 and parallel flush 2). Depending on the correctoptimizations for the backing store, flush process 10 may sort only therecords in each bucket into a parallel flush job, sort all records in aparallel flush job, etc. When all of the flush jobs are full or if thereare no buckets left in the LSNTree to flush, the parallel flush jobs maythen be sent to the backing store (Mapper) to be processed.

In some implementations, flush process 10 may flush 406 the portion ofthe log records to the backing store in parallel. For instance, andreferring at least to the example implementation of FIG. 11, an exampleflowchart 1100 associated with flush process 10 that describes thesequence of events that may occur on a flush request is shown. Similarto known systems, a flush request may be initiated when the log ring'slow watermark is reached. The dirty pages may be divided into parallelflush jobs to perform work and complete when all work is done. Eachparallel flush may open a new cache transaction. Each parallel flush mayissue holds to cache for each of the log records it intends to flush andmay then issue a flush request to the backing store for all pages held.

In some implementations, flushing the portion of the log records to thebacking store in parallel may include invalidating 410 the portion ofthe log records in memory, removing 412 the portion of the log recordsfrom the bucket, and/or removing 414 the portion of the log records fromthe tree. For example, on the completion of the flush 406 of the databuffers, the log may invalidate 410 the flushed page descriptors. Therecords may be invalidated in memory to indicate that the page is nolonger dirty. In some implementations, the record may also be removedfrom the bucket to which it belongs.

In some implementations, flush process 10 may rebalance 416 the tree.For instance, removal of records from the LSNTree may cause the lowestlog offset within the bucket to change or may cause a removal of thebucket from the LSNTree. The LSNTree should be rebalanced 416 for suchcases. When the processing of the flushed pages are done in memory, thelog may reclaim ring space by moving the tail beyond flushed pages up tothe next dirty descriptor, and the tail marker may be persisted on thecommit of the flush transaction. As part of the commit of the tailmarker, the tail may be updated on both nodes.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of thedisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. As used herein, the language “at least one of A, B,and C” (and the like) should be interpreted as covering only A, only B,only C, or any combination of the three, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps (notnecessarily in a particular order), operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps (not necessarily in a particular order),operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents (e.g., ofall means or step plus function elements) that may be in the claimsbelow are intended to include any structure, material, or act forperforming the function in combination with other claimed elements asspecifically claimed. The description of the present disclosure has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the disclosure in the formdisclosed. Many modifications, variations, substitutions, and anycombinations thereof will be apparent to those of ordinary skill in theart without departing from the scope and spirit of the disclosure. Theimplementation(s) were chosen and described in order to explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various implementation(s) with various modifications and/or anycombinations of implementation(s) as are suited to the particular usecontemplated.

Having thus described the disclosure of the present application indetail and by reference to implementation(s) thereof, it will beapparent that modifications, variations, and any combinations ofimplementation(s) (including any modifications, variations,substitutions, and combinations thereof) are possible without departingfrom the scope of the disclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method comprising:organizing a plurality of log records into a plurality of buckets,wherein each bucket is associated with a range of a plurality of rangeswithin a backing store; selecting a bucket of the plurality of bucketsfrom which a portion of the log records of the plurality of log recordsare to be flushed; organizing the portion of the log records intoparallel flush jobs; and flushing the portion of the log records to thebacking store in parallel.
 2. The computer-implemented method of claim 1wherein the portion of the log records include destination logical blockaddress (LBA) of the backing store associated with the range, andwherein the portion of the log records include log sequence numbers(LSNs).
 3. The computer-implemented method of claim 1 wherein selectingthe bucket of the plurality of buckets from which the portion of the logrecords of the plurality of log records are to be flushed includesselecting the bucket with a lowest LSN from a tree.
 4. Thecomputer-implemented method of claim 3 wherein flushing the portion ofthe log records to the backing store in parallel includes invalidatingthe portion of the log records in memory.
 5. The computer-implementedmethod of claim 4 wherein flushing the portion of the log records to thebacking store in parallel further includes removing the portion of thelog records from the bucket.
 6. The computer-implemented method of claim5 wherein flushing the portion of the log records to the backing storein parallel further includes removing the portion of the log recordsfrom the tree.
 7. The computer-implemented method of claim 6 furthercomprising rebalancing the tree.
 8. A computer program product residingon a computer readable storage medium having a plurality of instructionsstored thereon which, when executed across one or more processors,causes at least a portion of the one or more processors to performoperations comprising: organizing a plurality of log records into aplurality of buckets, wherein each bucket is associated with a range ofa plurality of ranges within a backing store; selecting a bucket of theplurality of buckets from which a portion of the log records of theplurality of log records are to be flushed; organizing the portion ofthe log records into parallel flush jobs; and flushing the portion ofthe log records to the backing store in parallel.
 9. The computerprogram product of claim 8 wherein the portion of the log recordsinclude destination logical block address (LBA) of the backing storeassociated with the range, and wherein the portion of the log recordsinclude log sequence numbers (LSNs).
 10. The computer program product ofclaim 8 wherein selecting the bucket of the plurality of buckets fromwhich the portion of the log records of the plurality of log records areto be flushed includes selecting the bucket with a lowest LSN from atree.
 11. The computer program product of claim 10 wherein flushing theportion of the log records to the backing store in parallel includesinvalidating the portion of the log records in memory.
 12. The computerprogram product of claim 11 wherein flushing the portion of the logrecords to the backing store in parallel further includes removing theportion of the log records from the bucket.
 13. The computer programproduct of claim 12 wherein flushing the portion of the log records tothe backing store in parallel further includes removing the portion ofthe log records from the tree.
 14. The computer program product of claim13 wherein the operations further comprise rebalancing the tree.
 15. Acomputing system including one or more processors and one or morememories configured to perform operations comprising: organizing aplurality of log records into a plurality of buckets, wherein eachbucket is associated with a range of a plurality of ranges within abacking store; selecting a bucket of the plurality of buckets from whicha portion of the log records of the plurality of log records are to beflushed; organizing the portion of the log records into parallel flushjobs; and flushing the portion of the log records to the backing storein parallel.
 16. The computing system of claim 15 wherein the portion ofthe log records include destination logical block address (LBA) of thebacking store associated with the range, and wherein the portion of thelog records include log sequence numbers (LSNs).
 17. The computingsystem of claim 15 wherein selecting the bucket of the plurality ofbuckets from which the portion of the log records of the plurality oflog records are to be flushed includes selecting the bucket with alowest LSN from a tree.
 18. The computing system of claim 17 whereinflushing the portion of the log records to the backing store in parallelincludes invalidating the portion of the log records in memory andremoving the portion of the log records from the bucket.
 19. Thecomputing system of claim 18 wherein flushing the portion of the logrecords to the backing store in parallel further includes removing theportion of the log records from the tree.
 20. The computing system ofclaim 19 wherein the operations further comprise rebalancing the tree.